Metadata-Version: 2.1
Name: DeepSurrogatepin
Version: 0.12
Summary: Deep surrogate model for the probability of informed trading model
Home-page: https://github.com/GuillaumePv/pin_surrogate_model
Author: Guillaume Pavé
Author-email: guillaumepave@gmail.com
License: MIT
Keywords: Machine learning, market microstructure
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: tensorflow
Requires-Dist: tqdm
Requires-Dist: tensorflow-probability

# Master thesis: Deep Structural estimation: with an application to market microstructure modelling

This package proposes an easy application of the master thesis: "Deep Structural estimation: with an application to market microstructure modelling"

## Authors

- Guillaume Pavé (HEC Lausanne,guillaumepave@gmail.com)

## Supervisors

- Simon Scheidegger (Department of Economics, HEC Lausanne, simon.scheidegger@unil.ch)
- Antoine Didisheim (Swiss Finance Institute, antoine.didisheim@unil.ch)

## Instructions

1) Download parameters of the surrogate (https://drive.google.com/drive/folders/1RTtYqOipJ-OJpveLu9Ui9NbYGvCDJtNL?usp=sharing)
2) Create a folder "model_save" and put parameters inside
3) Download training datatset "simulation_data_PIN.txt" from https://drive.google.com/file/d/1iUR-Zsd_UAo8bnZEMh5hpQ0SjYtpmtQA/view?usp=sharing
4) Create a folder "data" and put the dataset inside.
5) Now, you can use the train dataset or you could generate your own dataset (https://github.com/edwinhu/pin-code)

Github project is available at: https://github.com/GuillaumePv/pin_surrogate_model
If you find bugs, do not hesitate to create Issues inside of the github project.
